A significant amount of neural decoding work has sought to classify human fMRI data [1]}, [2]} and this work has generally proven fruitful given the high classification rates achieved by modern deep models. However, fMRI requires large, bulky, costly immobile machines. The availability of lower-cost, mobile systems is a requirement of any Brain-Computer Interface system, hence the recent push to perform a similar style of classification using electroencephalography (EEG) — an extensive review of these methods can be found in [3]}. However, EEG data tend to be difficult to classify accurately due to the low signal-to-noise ratio.
